


import math
import sys

import cva
import numpy as np
def 							UpperCamelCase_(					_A :np.ndarray					,  _A :float	)->		np.ndarray:
			# For applying gaussian function for each element in matrix.
			UpperCamelCase__       =							math.sqrt(_A	)
			UpperCamelCase__       =							1 / (sigma * math.sqrt(2 * math.pi	))
			return cons * np.exp(-((img / sigma) ** 2) * 0.5	)
def 							UpperCamelCase_(					_A :np.ndarray					,  _A :int					,  _A :int					,  _A :int	)->		np.ndarray:
			UpperCamelCase__       =							kernel_size // 2
			return img[x - half : x + half + 1, y - half : y + half + 1]
def 							UpperCamelCase_(					_A :int					,  _A :float	)->		np.ndarray:
			# Creates a gaussian kernel of given dimension.
			UpperCamelCase__       =							np.zeros((kernel_size, kernel_size)	)
			for i in range(0					,  _A	):
						for j in range(0					,  _A	):
									UpperCamelCase__       =							math.sqrt(
									    abs(i - kernel_size // 2	) ** 2 + abs(j - kernel_size // 2	) ** 2	)
			return vec_gaussian(_A					,  _A	)
def 							UpperCamelCase_(					_A :np.ndarray					,  _A :float					,  _A :float					,  _A :int					,  )->		np.ndarray:
			UpperCamelCase__       =							np.zeros(img.shape	)
			UpperCamelCase__       =							get_gauss_kernel(_A					,  _A	)
			UpperCamelCase__,   UpperCamelCase__       =							img.shape
			for i in range(kernel_size // 2					,  size_x - kernel_size // 2	):
						for j in range(kernel_size // 2					,  size_y - kernel_size // 2	):
									UpperCamelCase__       =							get_slice(_A					,  _A					,  _A					,  _A	)
									UpperCamelCase__       =							img_s - img_s[kernel_size // 2, kernel_size // 2]
									UpperCamelCase__       =							vec_gaussian(_A					,  _A	)
									UpperCamelCase__       =							np.multiply(_A					,  _A	)
									UpperCamelCase__       =							np.multiply(_A					,  _A	)
									UpperCamelCase__       =							np.sum(_A	) / np.sum(_A	)
									UpperCamelCase__       =							val
			return imga
def 							UpperCamelCase_(					_A :list	)->		tuple:
			UpperCamelCase__       =							args[1] if args[1:] else "../image_data/lena.jpg"
			UpperCamelCase__       =							float(args[2]	) if args[2:] else 1.0
			UpperCamelCase__       =							float(args[3]	) if args[3:] else 1.0
			if args[4:]:
						UpperCamelCase__       =							int(args[4]	)
						UpperCamelCase__       =							kernel_size + abs(kernel_size % 2 - 1	)
			else:
						UpperCamelCase__       =							5
			return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
						__UpperCamelCase		,	__UpperCamelCase		,	__UpperCamelCase		,	__UpperCamelCase     		=							parse_args(sys.argv)
						__UpperCamelCase     		=							cva.imread(filename, 0)
						cva.imshow('input image', img)

						__UpperCamelCase     		=							img / 2_5_5
						__UpperCamelCase     		=							out.astype('float32')
						__UpperCamelCase     		=							bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
						__UpperCamelCase     		=							out * 2_5_5
						__UpperCamelCase     		=							np.uinta(out)
						cva.imshow('output image', out)
						cva.waitKey(0)
						cva.destroyAllWindows()






__UpperCamelCase     		=							2_5_6
# Modulus to hash a string
__UpperCamelCase     		=							1_0_0_0_0_0_3
def 							UpperCamelCase_(					_A :str					,  _A :str	)->		bool:
			UpperCamelCase__       =							len(_A	)
			UpperCamelCase__       =							len(_A	)
			if p_len > t_len:
						return False

			UpperCamelCase__       =							0
			UpperCamelCase__       =							0
			UpperCamelCase__       =							1

			# Calculating the hash of pattern and substring of text
			for i in range(_A	):
						UpperCamelCase__       =							(ord(pattern[i]	) + p_hash * alphabet_size) % modulus
						UpperCamelCase__       =							(ord(text[i]	) + text_hash * alphabet_size) % modulus
						if i == p_len - 1:
									continue
						UpperCamelCase__       =							(modulus_power * alphabet_size) % modulus

			for i in range(0					,  t_len - p_len + 1	):
						if text_hash == p_hash and text[i : i + p_len] == pattern:
									return True
						if i == t_len - p_len:
									continue
						# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
						UpperCamelCase__       =							(
						    (text_hash - ord(text[i]	) * modulus_power) * alphabet_size
						    + ord(text[i + p_len]	)
						) % modulus
			return False
def 							UpperCamelCase_(					)->		None:
			UpperCamelCase__       =							"abc1abc12"
			UpperCamelCase__       =							"alskfjaldsabc1abc1abc12k23adsfabcabc"
			UpperCamelCase__       =							"alskfjaldsk23adsfabcabc"
			assert rabin_karp(_A					,  _A	) and not rabin_karp(_A					,  _A	)

			# Test 2)
			UpperCamelCase__       =							"ABABX"
			UpperCamelCase__       =							"ABABZABABYABABX"
			assert rabin_karp(_A					,  _A	)

			# Test 3)
			UpperCamelCase__       =							"AAAB"
			UpperCamelCase__       =							"ABAAAAAB"
			assert rabin_karp(_A					,  _A	)

			# Test 4)
			UpperCamelCase__       =							"abcdabcy"
			UpperCamelCase__       =							"abcxabcdabxabcdabcdabcy"
			assert rabin_karp(_A					,  _A	)

			# Test 5)
			UpperCamelCase__       =							"Lü"
			UpperCamelCase__       =							"Lüsai"
			assert rabin_karp(_A					,  _A	)
			UpperCamelCase__       =							"Lue"
			assert not rabin_karp(_A					,  _A	)
			print("Success."	)


if __name__ == "__main__":
						test_rabin_karp()


